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At least 181 records · Page 10
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NASA NTRS1986

Cumulus cloud properties derived using Landsat satellite data

Landsat Multispectral Scanner (MSS) digital data are used to remotely sense cumulus cloud properties such as cloud fraction and cloud reflectance, along with the distribution of cloud number and cloud fraction as a function of cloud size. The analysis is carried out for four cumulus fields covering regions approximately 150 km square. Results for these initial cloud fields indicate that: (1) the common intuitive model of clouds as nearly uniform reflecting surfaces is a poor representation of cumulus clouds, (2) the cumulus clouds were often multicelled, even for clouds as small as 1 km in diameter, (3) cloud fractional coverage derived using a simple reflectance threshold is sensitive to the chosen threshold even for 57-meter resolution Landsat data, (4) the sensitivity of cloud fraction to changes in satellite sensor resolution is less sensitive than suggested theoretically, and (5) the Landsat derived cloud size distributions show encouraging similarities among the cloud fields examined.

Wielicki, B. A.↗
NASA NTRS2021

Aerosol Properties in Cloudy Environments from Remote Sensing Observations: A Review of the Current State of Knowledge

Aerosol properties are fundamentally different near clouds than away from clouds. This paper reviews the current state of knowledge of aerosol properties in the near-low-cloud environment and quantitatively compares them with aerosols far from clouds, according to remote sensing observations. It interprets observations of aerosol properties from different sensors using satellite, aircraft, and ground-based observations. The correlation (and anticorrelation) between proximity to cloud and aerosol properties is discussed. Retrieval artifacts in the near-cloud environment are demonstrated and quantified for different sensor attributes and environmental conditions. Finally, the paper describes the possible corrections for near-cloud enhancement in remote sensing retrievals. This study is timely in view of science definition studies for NASA’s Aerosol, Cloud, Convection and Precipitation (ACCP) mission, which will also seek to directly link aerosol properties to nearby clouds.

A. Marshak↗
NASA NTRS1992

Satellite Analyses of Cirrus Cloud Properties During the FIRE Phase 2 Cirrus Intensive Field Observations over Kansas

The First ISCCP (International Satellite Cloud Climatology Project) Regional Experiment (FIRE) Phase II Intensive Field Observations (IFO) were taken over southeastern Kansas between November 13 and December 7,1991, to determine cirrus cloud properties. The observations include in situ microphysical data; surface, aircraft, and satellite remote sensing; and measurements of divergence over meso- and smaller-scale areas using wind profilers. Satellite remote sensing of cloud characteristics is an essential aspect for understanding and predicting the role of clouds in climate variations. The objectives of the satellite cloud analysis during FIRE are to validate cloud property retrievals, develop advanced methods for extracting cloud information from satellite-measured radiances, and provide multiscale cloud data for cloud process studies and for verification of cloud generation models. This paper presents the initial results of cloud property analyses during FIRE-II using Geostationary Operational Environmental Satellite (GOES) data and NOAA Advanced Very High Resolution Radiometer (AVHRR) radiances.

Minnis, Patrick↗
NASA NTRS1990

Cloud microphysical properties from multispectral reflectance measurements

Images as well as statistical properties of the cloud optical thickness and effective particle radius derived from reflected solar radiation measurements at 0.75, 1.65, and 2.16 micron are presented. The remote-sensing-derived cloud optical thickness and effective radius are compared with the values inferred from nearly simultaneous in situ microphysical measurements obtained from an aircraft. A good spatial correlation is observed for both the optical thickness and effective radius, it is noted that the remote sensing has a tendency to overestimate the effective radius; the optical thickness is overestimated for small optical thicknesses and underestimated for large optical thicknesses. The marginal probability density functions of optical thickness and effective radius are derived from the remote-sensing results and discussed.

King, Michael D.↗
NASA NTRS2001

Do Fractal Models of Clouds Produces the Right 3D Radiative Effects?

Stochastic fractal models of clouds are often used to study 3D radiative effects and their influence on the remote sensing of cloud properties. Since it is important that the cloud models produce a correct radiative response, some researchers require the model parameters to match observed cloud properties such as scale-independent optical thickness variability. Unfortunately, matching these properties does not necessarily imply that the cloud models will cause the right 3D radiative effects. First, the matched properties alone only influence the 3D effects but do not completely determine them. Second, in many cases the retrieved cloud properties have been already biased by 3D radiative effects, and so the models may not match the true real clouds. Finally, the matched cloud properties cannot be considered independent from the scales at which they have been retrieved. This paper proposes an approach that helps ensure that fractal cloud models are realistic and produce the right 3D effects. The technique compares the results of radiative transfer simulations for the model clouds to new direct observations of 3D radiative effects in satellite images.

Varnai, Tamas↗
NASA NTRSDate not supplied

An Observationally-Based Determination of the Arctic Sea Ice-Cloud Feedback Since 2000: Isolating the Arctic Cloud Response to Sea Ice Loss

Arctic sea ice responds to and drives Arctic climate change. The interactions between Arctic sea ice and clouds represent a mechanism through which sea ice can drive climate change. We composite active remote sensing satellite cloud properties for ice-free, marginal ice zone (MIZ), and ice-covered surfaces during MIZ crossing events to investigate the influence of the transition from an ice-covered to an ice-free surface on low-level clouds. We demonstrate that the event-based methodology controls for large-scale meteorological factors and isolates the sea ice effect on clouds. We find larger cloud fraction and total water content below ~1.5 km over ice-free relative to ice-covered surfaces during non-summer months, indicating a low-level cloud sensitivity to Arctic sea ice decline. During summer, results show larger cloud fraction and water content over ice-free surfaces, however the differences are statistically indistinguishable. Evidence is provided that atmospheric thermodynamic profile differences cause the cloud property differences, namely that ice-free footprints are warmer, moister, have more positive surface turbulent fluxes and are less stable than their ice-covered counterparts. Ice-free and ice-covered surface cloud property differences scale with surface temperature differences such that cloud property differences are only found in the presence of a surface temperature difference. We conclude that surface temperature differences modulate the cloud response to sea ice loss through influences on surface turbulent fluxes and lower tropospheric stability. The results imply a positive non-summer sea ice-cloud feedback and that up to a 0.02 cloud fraction and 0.05 g m 3 total water content increase in fall are due to the observed sea ice decline.

Patrick C Taylor↗
NASA NTRSDate not supplied

Estimating the Arctic Cloud-Sea Ice Feedback With Observations during the EOS Period

Arctic sea ice responds to and drives Arctic climate change. The interactions between Arctic sea ice and clouds represent a mechanism through which sea ice can drive climate change. We composite active remote sensing satellite cloud properties for ice-free, marginal ice zone (MIZ), and ice-covered surfaces during MIZ crossing events to investigate the influence of the transition from an ice-covered to an ice-free surface on low-level clouds. We demonstrate that the event-based methodology controls for large-scale meteorological factors and isolates the sea ice effect on clouds. We find larger cloud fraction and total water content below ~1.5 km over ice-free relative to ice-covered surfaces during non-summer months, indicating a low-level cloud sensitivity to Arctic sea ice decline. During summer, results show larger cloud fraction and water content over ice-free surfaces, however the differences are statistically indistinguishable. Evidence is provided that atmospheric thermodynamic profile differences cause the cloud property differences, namely that ice-free footprints are warmer, moister, have more positive surface turbulent fluxes and are less stable than their ice-covered counterparts. Ice-free and ice-covered surface cloud property differences scale with surface temperature differences such that cloud property differences are only found in the presence of a surface temperature difference. We conclude that surface temperature differences modulate the cloud response to sea ice loss through influences on surface turbulent fluxes and lower tropospheric stability. The results imply a positive non-summer sea ice-cloud feedback and that up to a 0.02 cloud fraction and 0.05 g m 3 total water content increase in fall are due to the observed sea ice decline.

Patrick C. Taylor↗
NASA NTRS2024

Correction of Cloud Optical Thickness Retrievals from Nadir Reflectances in the Presence of 3D Radiative Effects. Part I: Concept and Tests on 3D RT Simulations

3D effects cause substantial underestimation of cloud optical thickness (COT) in airborne and satellite retrievals based on 1D radiative transfer computations (such as in the case of widely used bispectral technique). For a single-layer isolated cloud we propose a simple linear correction of the retrieved COT with the renormalization factor dependent on the cloud’s aspect ratio (the ratio between vertical and horizontal dimensions of the cloud). This is an empirical assumption which we successfully test using synthetic 3D RT data. We introduce a heuristic “block model” of 3D radiative effects and show that the functional form of the renormalization factor is consistent with the process of radiation escape from cloud sides in an essentially 3D geometry. We also extend the block model to the case of single-layer broken cloud field with radiative interaction between the neighboring clouds. In this case the renormalization factor depends also on the distance between clouds.

cloud optical thickness↗
NASA NTRS2018

Three-Dimensional Sensor Forward Modeling of Clouds and Precipitation in the Multi-Instrument Inverse Solver Testbed (MIIST)

Sensor forward models are an essential tool for interpreting remote sensing observations and performing quantitative estimates of geophysical parameters. Our three-dimensional forward modeling and retrieval framework allows us to perform detailed analyses of NASA field campaign datasets for a deeper understanding of the remote sensing of clouds and precipitation. This presentation details the componenets of this radiative transfer model used to simulate active (radar) and passive (microwave radiometer) observations, and we give some relevant examples based on both model precipitation systems and actual observations.

Adams, Ian S.↗
NASA NTRS2016

Airborne Spectral BRDF of Various Surface Types (Ocean, Vegetation, Snow, Desert, Wetlands, Cloud Decks, Smoke Layers) for Remote Sensing Applications

In this paper we describe measurements of the bidirectional reflectance-distribution function (BRDF) acquired over a 30-year period (1984-2014) by the National Aeronautics and Space Administration's (NASA's) Cloud Absorption Radiometer (CAR). Our BRDF database encompasses various natural surfaces that are representative of many land cover or ecosystem types found throughout the world. CAR's unique measurement geometry allows a comparison of measurements acquired from different satellite instruments with various geometrical configurations, none of which are capable of obtaining such a complete and nearly instantaneous BRDF. This database is therefore of great value in validating many satellite sensors and assessing corrections of reflectances for angular effects. These data can also be used to evaluate the ability of analytical models to reproduce the observed directional signatures, to develop BRDF models that are suitable for sub-kilometer-scale satellite observations over both homogeneous and heterogeneous landscape types, and to test future spaceborne sensors. All of these BRDF data are publicly available and accessible in hierarchical data format (http:car.gsfc.nasa.gov/).

albedo↗
NASA NTRS1999

Diffuse Reflection of Laser Light From Clouds

Laser light reflected from an aqueous suspension of particles or "cloud" with known thickness and particle size distribution defines the "cloud radiative Green's function", G. G is sensitive to cloud thickness, allowing retrieval of that important quantity. We describe a laboratory simulation of G, useful in design of an offbeam Lidar instrument for remote sensing of cloud thickness. Clouds of polystyrene microspheres suspended in water are analogous to real clouds of water droplets suspended in air. The size distribution extends from 0.5 microns to 25 microns, roughly lognormal, similar to real clouds. Density of suspended spheres is adjusted so photon mean-free-path is about 10 cm, 1000 times smaller than in real clouds. The light source is a Nd:YAG laser at 530 nm. Detectors are flux and photon-counting PMTs, with a glass probe for precise positioning. A Labview 5 VI controls position and data acquisition, via an NI Motion Control board connected to a stepper motor driving an Edmund linear slider,and a 16-channel 16-bit NI-DAQ board. The stepper motor is accurate to 10 microns. Step size is selectable. Far from the beam, the rate of exponential increase in the beam direction scales as expected from diffusion theory, linearly with cloud thickness, and inversely as the square root of the reduced optical thickness, independent of particle size. Nearer the beam the signal increases faster than exponential and depends on particle size. Results verify 3D Monte Carlo simulations that demonstrate detectability of remotely sensed offbeam returns, without filters at night, with narrow bandpass filter in day.

Cahalan, R. F.↗
NASA NTRS1999

Satellite Remote Sensing of Aerosol Forcing

Aerosol and cloud impacts on the earth's climate become a recent hot topic in climate studies. Having near future earth observing satellites, EOS-AM1 (Earth Observing System-AM1), ENVISAT (Environmental Satellites) and ADEOS-2 (Advanced Earth Observation Satellite-2), it will be a good timing to discuss how to obtain and use the microphysical parameters of aerosols and clouds for studying their climate impacts. Center for Climate System Research (CCSR) of the University of Tokyo invites you to 'Symposium on synergy between satellite-remote sensing and climate modeling in aerosol and cloud issues.' Here, we like to discuss the current and future issues in the remote sensing of aerosol and cloud microphysical parameters and their climate modeling studies. This workshop is also one of workshop series on aerosol remote sensing held in 1996, Washington D. C., and Meribel, France in 1999. It should be reminded that NASDA/ADEOS-1 & -2 (National Space Development Agency of Japan/Advanced Earth Observation Satellite-1 & -2) Workshop will be held in the following week (Dec. 6-10, 1999), so that this opportunity will be a perfect period for you to attend two meetings for satellite remote sensing in Japan. A weekend in Kyoto, the old capital of Japan, will add a nice memory to your visiting Japan. *Issues in the symposium: 1) most recent topics in aerosol and cloud remot sensing, and 2) utility of satellite products on climate modeling of cloud-aerosol effects.

Remer, Lorraine↗
NASA NTRS2014

TPSAS-NF1676L-19259-DND

In climate and Earth energy budget studies, understanding the presence and distribution of clouds is an essential first step. AVHRR measurements comprise the longest record of imager data from a common instrument for remote sensing of cloud and radiation properties. The NASA LaRC AVHRR cloud mask is a global cloud detection algorithm used as part of the Climate Data Record (CDR) project initiated by NOAA's National Climatic Data Center (NCDC). The objective of CDR program is to provide climate information derived from weather satellite data for more than 30 years. Recent improvements in the LaRC AVHRR cloud mask include detecting more daytime small cumulus and thin Cirrus in tropical oceans, especially over Sun Glint regions, better discrimination between polar clouds and snow ice surfaces including nighttime over Antarctica where 3.7 μm signals are noisy, and enhancement of nighttime ocean thin Cirrus and low clouds detection. Monthly averaged global and zonal cloud fractions from improved NOAA 18 AVHRR cloud mask compare well with CERES-MODIS Edition 4 (CM Ed4), MODIS team, and CALIPSO cloud mask. Spatially and temporally matched cloud detection statistics between NOAA 18 AVHRR and CALIPSO cloud mask will also be presented.

Qing Z Trepte↗
NASA NTRS2012

The Physics of Imaging with Remote Sensors : Photon State Space & Radiative Transfer

Standard (mono-pixel/steady-source) retrieval methodology is reaching its fundamental limit with access to multi-angle/multi-spectral photo- polarimetry. Next... Two emerging new classes of retrieval algorithm worth nurturing: multi-pixel time-domain Wave-radiometry transition regimes, and more... Cross-fertilization with bio-medical imaging. Physics-based remote sensing: - What is "photon state space?" - What is "radiative transfer?" - Is "the end" in sight? Two wide-open frontiers! center dot Examples (with variations.

clouds↗
NASA NTRS1982

Effect of subpixel-sized cloud on target discrimination from satellite data

The paper reports results of a study of the effects of subpixel-sized clouds on remotely sensed radiance; the NOAA-7 Advanced Very High Resolution Radiometer was used in the study. It is noted that it is quite possible that clouds could exist with dimensions of less than 1 x 1 km, which will not be resolvable as discrete pixels filled only by cloud, but which will be recorded as pixels with radiance values not characteristic of the ground target. It is shown that for a clear atmosphere the presence of even 20% cloud produces a fivefold decrease or worse in vegetative index at all scan angles for both pure and mixed targets. These effects are large and show that undetected cloud can produce drastic changes in combinations of recorded radiance used for vegetative assessment.

Duggin, M. J.↗
NASA NTRS1998

Off-beam (multiply-scattered) lidar returns from stratus: Cloud-information content and sensitivity to noise - 1

We review the basic multiple scattering theory of off-beam lidar returns from optically thick clouds using the diffusion approximation. The shape of the temporal signal - the stretched pulse - depends primarily on the physical thickness of the cloud whereas its spatial counterpart - the diffuse spot - conveys specific information on the cloud's optical thickness, as do the absolute returns. This makes observation of the weak off-beam lidar returns an attractive prospect in remote sensing of cloud properties. By estimating the signal-to-noise ratio, we show that night-time measurements can be performed with existing technology. By the same criterion, day-time operation is a challenge that can only be met with a combination of cutting-edge techniques in filtering and in laser sources.

Davis, Anthony B.↗
NASA NTRS2014

OMMYDCLD: a New A-train Cloud Product that Co-locates OMI and MODIS Cloud and Radiance Parameters onto the OMI Footprint

Clouds cover approximately 60% of the earth's surface. When obscuring the satellite's field of view (FOV), clouds complicate the retrieval of ozone, trace gases and aerosols from data collected by earth observing satellites. Cloud properties associated with optical thickness, cloud pressure, water phase, drop size distribution (DSD), cloud fraction, vertical and areal extent can also change significantly over short spatio-temporal scales. The radiative transfer models used to retrieve column estimates of atmospheric constituents typically do not account for all these properties and their variations. The OMI science team is preparing to release a new data product, OMMYDCLD, which combines the cloud information from sensors on board two earth observing satellites in the NASA A-Train: Aura/OMI and Aqua/MODIS. OMMYDCLD co-locates high resolution cloud and radiance information from MODIS onto the much larger OMI pixel and combines it with parameters derived from the two other OMI cloud products: OMCLDRR and OMCLDO2. The product includes histograms for MODIS scientific data sets (SDS) provided at 1 km resolution. The statistics of key data fields - such as effective particle radius, cloud optical thickness and cloud water path - are further separated into liquid and ice categories using the optical and IR phase information. OMMYDCLD offers users of OMI data cloud information that will be useful for carrying out OMI calibration work, multi-year studies of cloud vertical structure and in the identification and classification of multi-layer clouds.

OMMYDCLD↗